ConCur: Knowledge Base Construction and Curation

知识图是图形结构的知识资源，通常表示为三元组，如（“UK”，“hasCapital”，“London”）和（“London”，“instanceOf”，“City”）。除了这些基本的“事实”，知识图通常还包括关于领域的结构性知识，通常基于实体类型的层次结构（又名类或概念）；例如，（“City”，“subClassOf”，“HumanSettlement”）。大部分或全部由结构性知识组成的知识图通常称为本体。一些知识图是通用的，如Wikidata和谷歌知识图，而另一些则是为特定领域（如医学）开发的。它们的重要性正在迅速增加，并在许多应用中发挥着关键作用。例如，b谷歌将其知识图谱用于搜索、问答和谷歌助手，而亚马逊和苹果也分别使用知识图谱为他们的个人助理Alexa和Siri提供动力。知识图谱广泛用于健康和福祉领域，例如，用于组织和交换信息，并为临床人工智能（AI）提供动力。一个例子是FoodOn，它是一个表示食品知识（如细粒度食品分类、营养和过敏原）以及相关活动（如农业）的本体。然而，知识图谱的构建和维护是非常具有挑战性的，并且可能需要大量的人力。尽管知识创造的成本很高，但知识图往往仍然是有偏差的、不完整的或过于粗粒度的。以健康和生活方式本体HeLis为例。它的食物知识非常简单，通常用一个实体代表许多不同的变体（例如，“香蕉”代表所有种类和香蕉的衍生物），与专门的生物医学本体相比，它的健康知识非常不完整。此外，在知识图中很难避免错误，如不正确的事实和分类；例如，FoodOn将豆浆归类为一种牛奶，而不是一种豆制品。这些错误可能是信息来源的遗传，也可能是施工过程造成的。这些问题严重影响了知识图的有用性和使用它们的系统的可靠性；例如，如果知识图谱用于食品过敏原警报系统，豆浆的分类可能是危险的。因此，有效的知识图谱构建与管理势在必行，对充分发挥知识图谱的价值起着至关重要的作用。由于现在有许多可用的知识资源，一种可能的方法是使用多个资源来处理覆盖和质量问题，例如，通过集成和交叉检查。例如，将HeLis与FoodOn集成，可以将食品（包括香蕉）的细粒度分类与生活方式知识结合起来。此外，将FoodOn与HeLis进行交叉检查将发现豆浆的问题，而豆浆在HeLis中被正确地归类为豆制品。知识资源的自动化集成具有挑战性，但是结合语义和基于学习的技术似乎是一个非常有前途的方法，并且我们已经在这个方向上获得了一些令人鼓舞的初步结果。因此，拟议的研究将研究一系列语义和机器学习技术，以及如何将它们结合起来支持知识图谱的构建和管理。除了将其应用于知识图谱的构建和管理之外，本研究还将促进新的神经符号理论、范式和方法的发展，例如用于表达性知识的学习表示的深度语义嵌入，以及用于解决样本短缺问题的知识引导学习。这些技术有望彻底改变许多人工智能和大数据技术。

Knowledge graphs are graph-structured knowledge resources which are often expressed as triples such as ("UK", "hasCapital", "London") and ("London", "instanceOf", "City"). As well as such basic "facts", knowledge graphs often include structural knowledge about the domain, typically based on a hierarchy of entity types (AKA classes or concepts); e.g., ("City", "subClassOf", "HumanSettlement"). A knowledge graph that consist largely or wholly of structural knowledge is often called an ontology.Some knowledge graphs are general purpose, such as Wikidata and the Google knowledge graph, while others are developed for specific domains such as medicine. They are rapidly gaining in importance and are playing a key role in many applications. For example, Google uses its knowledge graph for search, question answering and Google Assistant, while Amazon and Apple also use knowledge graphs to power their personal assistants Alexa and Siri, respectively. Knowledge graphs are widely used in the domain of health and wellbeing, e.g., for organising and exchanging information and to power clinical artificial intelligence (AI). One example is FoodOn, an ontology representing food knowledge such as fine-grained food product categorization, nutrition and allergens, as well as related activities such as agriculture.Knowledge graph construction and maintenance is, however, very challenging, and may require a considerable amount of human effort. Notwithstanding the high cost of knowledge creation, knowledge graphs are often still biased, incomplete or too coarse-grained. Take HeLis, an ontology for health and lifestyle, as an example. Its food knowledge is quite simple and often represents many different variants with a single entity (e.g., "Banana" for all kinds and derivatives of bananas), and its knowledge of health is highly incomplete when compared with dedicated biomedical ontologies. In addition, it is hard to avoid errors such as incorrect facts and categorisations in knowledge graphs; e.g., FoodOn categorises soy milk as a kind of milk, but not as a kind of soy product. Such errors may be inherited from the information source or be caused by the construction procedure. These issues significantly impact the usefulness of knowledge graphs and the reliability of the systems that use them; e.g., the categorisation of soy milk could be dangerous if the knowledge graph were used in a food allergen alert system.Therefore, effective knowledge graph construction and curation is urgently required and will play a critical role in exploiting the full value of knowledge graphs. As there are now many available knowledge resources, one possible approach is to use multiple sources to address both coverage and quality issues, e.g., via integration and cross-checking. For example, integrating HeLis with FoodOn would combine fine-grained categorization of food products (including bananas) with lifestyle knowledge. Moreover, cross-checking FoodOn with HeLis will reveal the problem with soy milk, which is correctly categorized as a soy product in HeLis. Automating the integration of knowledge resources is challenging, but combining semantic and learning-based techniques seems to be a very promising approach, and we have already obtained some encouraging preliminary results in this direction.The proposed research will therefore study a range of semantic and machine learning techniques, and how to combine them to support knowledge graph construction and curation. As well as its application to knowledge graph construction and curation, this research will also contribute to the development of new neural-symbolic theories, paradigms and methods, such as deep semantic embedding for learning representations for expressive knowledge, and knowledge-guided learning for addressing sample shortage problems. These techniques promise to revolutionize many AI and big data technologies.